Abstract

This paper explores integrating quantum technologies with semantic communications to address the challenges of transmitting knowledge graph embeddings over traditional communication channels. As communication systems evolve to facilitate knowledge sharing and processing among natural and artificial intelligent agents, they encounter issues such as inefficiency, inaccuracy, and high complexity. To address these challenges, we propose a novel approach called quantum semantic communications, which integrates quantum technologies into the emerging framework of semantic communications. The primary goal of this protocol is to transmit semantic features efficiently, minimizing resource consumption while ensuring robustness. Our approach involves extracting semantic data from knowledge graphs using pre-trained large language model encoder and graph neural network as semantic encoders to generate compact knowledge representations transmitted over quantum communication channels. To assess the effectiveness of our protocol, we analyze performance metrics such as quantum semantic fidelity and F1 score under various noise conditions, including depolarizing, amplitude damping, and dephasing. Our simulations show that the proposed quantum semantic communication approach remains robust even in a low signal-to-noise regime.